“Leaving no one behind”: COVID-19 Response in Black Canadian Communities
Bibliographic record
Abstract
Despite the universal healthcare system in Canada, Canadians of African Descent (CAD) still face numerous problems that place them at higher risk to pandemics such as COVID-19. From the struggles of working as frontline workers, to challenges compounded by pre-existing chronic medical conditions such as Diabetes, CAD may face unique issues, further weighing on their existing and potential health outcomes. This situation calls for closer attention to the specific needs of CAD who may be at greater risk of late diagnosis and delayed treatment for COVID-19. Historically, marginalized communities such as CAD must be included in healthcare considerations and planning, so as to avoid further leaving them behind during and after the storm. Past evidence has shown that structural inequities shape who is affected by disease and its economic fallout. Therefore, the unique needs of CAD must be considered in healthcare planning with the ongoing COVID-19 response. Keywords: pandemic, marginalized, healthcare, COVID-19, Canadians of African Descent
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.060 | 0.018 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".